Gerrit Review

This MCP server provides integration with Gerrit code review system, allowing AI assistants to review code changes and their details through a simple interface.

python

GitHub Stars

python

Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "cayirtepeomer-gerrit-code-review-mcp": {
      "command": "/path/to/your/workspace/gerrit-code-review-mcp/.venv/bin/python",
      "args": [
        "/path/to/your/workspace/gerrit-code-review-mcp/server.py",
        "--transport",
        "stdio"
      ],
      "env": {
        "PATH": "/path/to/your/workspace/gerrit-code-review-mcp/.venv/bin:/usr/local/bin:/usr/bin:/bin",
        "PYTHONPATH": "/path/to/your/workspace/gerrit-code-review-mcp",
        "GERRIT_HOST": "gerrit.example.com",
        "GERRIT_USER": "your-username",
        "VIRTUAL_ENV": "/path/to/your/workspace/gerrit-code-review-mcp/.venv",
        "GERRIT_CA_BUNDLE": "/path/to/ca.pem",
        "GERRIT_SSL_VERIFY": "true",
        "GERRIT_HTTP_PASSWORD": "your-http-password",
        "GERRIT_EXCLUDED_PATTERNS": "\\.pbxproj$,\\.xcworkspace$,node_modules/"
      }
    }
  }
}

You can integrate Gerrit code reviews with an MCP server to let AI assistants fetch complete change details, inspect diffs across patchsets, and submit reviews directly from your tooling. This enables fast, contextual code reviews and consistent collaboration across your Gerrit workflows.

How to use

You interact with the Gerrit Review MCP Server through your MCP client to perform three core actions: fetch complete change details, compare patchset differences, and submit review feedback. Start by requesting a change to see the full metadata, file diffs, and review history. Then compare specific patchsets to understand how changes evolved over iterations. Finally, post a summary with optional inline comments and votes to streamline the review cycle.

How to install

Prerequisites for running the Gerrit Review MCP Server are Python 3.10 or higher and access to Gerrit via HTTP with appropriate credentials. You also need access to the MCP client transport you plan to use (stdio in this guide). All commands below assume you are operating in a Unix-like shell; adapt paths as needed for Windows.

npx -y @smithery/cli install @cayirtepeomer/gerrit-code-review-mcp --client claude

If you prefer a manual setup, follow these steps. Create a project workspace, set up a virtual environment, and install the MCP package in editable mode.

git clone <repository-url>
cd gerrit-review-mcp

python -m venv .venv
# For macOS/Linux:
source .venv/bin/activate
# For Windows:
.venv\Scripts\activate

pip install -e .

Auto-start via stdio transport

The MCP configuration uses a local stdio server started with your Python virtual environment. Ensure your paths reflect your workspace so the server can start correctly.

/path/to/your/workspace/gerrit-code-review-mcp/.venv/bin/python "/path/to/your/workspace/gerrit-code-review-mcp/server.py" --transport stdio

Manual MCP configuration example

Use this JSON structure to register the Gerrit Review MCP Server with your MCP client configuration.

{
  "mcpServers": {
    "gerrit_review": {
      "type": "stdio",
      "command": "/path/to/your/workspace/gerrit-code-review-mcp/.venv/bin/python",
      "args": [
        "/path/to/your/workspace/gerrit-code-review-mcp/server.py",
        "--transport",
        "stdio"
      ],
      "cwd": "/path/to/your/workspace/gerrit-code-review-mcp",
      "env": {
        "PYTHONPATH": "/path/to/your/workspace/gerrit-code-review-mcp",
        "VIRTUAL_ENV": "/path/to/your/workspace/gerrit-code-review-mcp/.venv",
        "PATH": "/path/to/your/workspace/gerrit-code-review-mcp/.venv/bin:/usr/local/bin:/usr/bin:/bin"
      }
    }
  }
}

What you can do with the MCP server

With this MCP server, you can fetch the complete change information, including files, patch sets, author/reviewer data, and review history. You can compare patchset diffs to track changes across iterations and submit review feedback with optional inline comments and voting labels.

Available tools

fetch_gerrit_change

Fetches complete change information including files and patch sets; returns change details with author/reviewer data and diff content.

fetch_patchset_diff

Compares differences between two patchsets for a change; can target a specific file or all changed files.

submit_gerrit_review

Posts summary feedback with optional labels and inline comments; can target a specific patchset and control notification scope.

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VeilStrat

AI signals for GTM teams